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Journal articles on the topic 'Scheduling. Production scheduling. Production scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Fithri, Prima, and Fitri Ramawinta. "Penjadwalan Mesin dengan Menggunakan Algoritma Pembangkitan Jadwal Aktif dan Algoritma Penjadwalan Non-Delay untuk Produk Hydrotiller dan Hammermil pada CV. Cherry Sarana Agro." Jurnal Optimasi Sistem Industri 12, no. 2 (2016): 377. http://dx.doi.org/10.25077/josi.v12.n2.p377-399.2013.

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Fulfillment of all demands of consumers who come to the product is one thing that always wanted to be achieved by a company. These requests are not independent of the company's ability to manufacture certain products. CV Cherry Sarana Agro manufactures a wide range of agricultural equipment, one of which is the product hydrotiller and hammermil. Demand for both products are always in large numbers for each period, however, the company could not meet the entire demand. One of the main factors that led this small company's production capacity for these two products is not optimal scheduling of machines made by companies, causing many to be a queue on a particular machine so that the total process operating time becomes very large. Scheduling method is used to optimize the scheduling of machines working on the report of this practice is actively scheduling method and the method of non-delay scheduling. The data needed to perform scheduling with both of these methods is the data used machines, data processing operations and data processing time of operation. With these three data, can be compared to the actual scheduling done by the company with the scheduling is done using active scheduling method and the method of non-delay scheduling. The most optimal scheduling is obtained after comparing the three methods used are scheduling using the non-delay scheduling. This method was chosen because the resulting make span is much smaller than the two other methods. This method is well applied in the company because in addition to reducing the total processing time, can also increase production capacity, so that all requests can be met.Keywords: Active schedulling method, non-delay schedulling method, makespan, hydrotiller, hammermil

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2

Li, Yingying. "Research on game scheduling of galvanizing pipe production." Functional materials 24, no. 3 (2017): 005–495. http://dx.doi.org/10.15407/fm24.03.490.

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3

Kromodihardjo, Sudiyono, and Ergo Swasono Kromodihardjo. "Modeling of Well Service and Workover to Optimize Scheduling of Oil Well Maintenance." Applied Mechanics and Materials 836 (June 2016): 311–16. http://dx.doi.org/10.4028/www.scientific.net/amm.836.311.

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Well maintenance (well service and workover) is an operation needed by oil company to guarantee the optimum productionof its oil well.Well maintenance is performed using large equipment called hydraulic workover unit (HWU-Rig) which is available in limited number. Scheduling sequence of the HWU-Rig to do well service must meet the goal of the maintenance that is to minimize the loss of oil well production due to well breakdown. Thus minimizing breakdown time of well with high rate production is a priority. However, scheduling secuence of the HWU-Rig to perform its task for few days ahead become complicated due to the numerous alternatives of secuence to choose. Each alternatives of sequence yields a certain production loss. Arbitrarily scheduling sequence may not yield the goal og minimizing the loss of well production. This research was done by analyzing workover scheduling system and data from Kondur Petroleum such as well location, well production rate, and service time needed to be performed on wells. Algorithm to create schedulling sequence was developed in the research. The algorithm was then implemented in discrete simulation software, and yield the result of absolute global optimal solution, near optimal solution and local optimal solution of the HWU scheduling problem.

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4

Paprocka, Iwona, and Bożena Skołud. "Robust Scheduling, a Production Scheduling Model of Failures." Applied Mechanics and Materials 307 (February 2013): 443–46. http://dx.doi.org/10.4028/www.scientific.net/amm.307.443.

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In the paper a production model with failures is presented where successive failure-free times are supposed to have normal distributions and are followed by normally distributed times of repairs. Unknown parameters of the distribution are estimated using e.g. empirical moments approach. Predictions of unknown parameters are done using classical regression method. Having Mean Time To First Failure, and Mean Time of Repair a disturbance robust predictive schedule is generated using an immune algorithm and rule Minimal Impact of Disturbed Operation on the Schedule.

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5

Zaremba, Marek B. "Scheduling of production processes." Control Engineering Practice 4, no. 1 (1996): 141–42. http://dx.doi.org/10.1016/s0967-0661(96)90035-0.

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6

Nussbaum, Miguel, and Eduardo A. Parra. "A Production Scheduling System." ORSA Journal on Computing 5, no. 2 (1993): 168–81. http://dx.doi.org/10.1287/ijoc.5.2.168.

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7

Mauergauz, Yuri. "Scheduling for production teams." International Journal of Industrial Engineering Computations 6, no. 3 (2015): 339–50. http://dx.doi.org/10.5267/j.ijiec.2015.3.001.

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8

Foote, B. L., A. Ravindran, and S. Lashine. "Production planning & scheduling." Computers & Industrial Engineering 15, no. 1-4 (1988): 129–38. http://dx.doi.org/10.1016/0360-8352(88)90075-7.

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9

Hastings, N. A. J., and C. H. Yeh. "Job oriented production scheduling." European Journal of Operational Research 47, no. 1 (1990): 35–48. http://dx.doi.org/10.1016/0377-2217(90)90087-r.

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10

Kolisch, Rainer, Marcus Brandenburg, and Claus Krüger. "Numetrix/3 Production Scheduling." OR Spektrum 22, no. 3 (2000): 307–12. http://dx.doi.org/10.1007/pl00013336.

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11

Taunton, J. C., and C. M. Ready. "Intelligent dynamic production scheduling." Food Research International 27, no. 2 (1994): 111–16. http://dx.doi.org/10.1016/0963-9969(94)90151-1.

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12

Schmidt, Günter. "Modelling production scheduling systems." International Journal of Production Economics 46-47 (December 1996): 109–18. http://dx.doi.org/10.1016/0925-5273(95)00019-4.

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13

Hruby, HF, and DM Panton. "Scheduling transfer champagne production." Omega 21, no. 6 (1993): 691–97. http://dx.doi.org/10.1016/0305-0483(93)90010-i.

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14

Oike, Shunsuke, Tomohisa Tanaka, Jiang Zhu, and Yoshio Saito. "Robust Production Scheduling Using Autonomous Distributed Systems." Key Engineering Materials 516 (June 2012): 166–69. http://dx.doi.org/10.4028/www.scientific.net/kem.516.166.

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This research proposes a method of production scheduling using autonomous distributed systems. A concrete message protocol is proposed to realize the production scheduling which includes not only Machine but also Human and AGV scheduling. Moreover this method realizes real time scheduling and parallel scheduling. Therefore, a new structure of production scheduling is proposed, which can realize a change of the type of production scheduler to correspond with a type of production system.

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15

Brown, J. R., and C. O. Ozgur. "Priority class scheduling: Production scheduling for multi-objective environments." Production Planning & Control 8, no. 8 (1997): 762–70. http://dx.doi.org/10.1080/095372897234650.

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16

Simeonov, S., and J. Simeonovová. "Simulation scheduling in food industry application." Czech Journal of Food Sciences 20, No. 1 (2011): 31–37. http://dx.doi.org/10.17221/3506-cjfs.

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Nowadays manufacturers are facing rapid and fundamental changes in the ways business is done. Producers are looking for simulation systems increasing throughput and profit, reducing cycle time, improving due-date performance, reducing WIP, providing plant-wide synchronization, etc. Planning and scheduling of coffee production is important for the manufacturer to synchronize production capacity and material inputs to meet the delivery date promised to the customer. A simulation model of coffee production was compiled. It includes roasting, grinding and packaging processes. Using this model the basic features of the coffee production system are obtained. An optimization module of the simulation SW is used for improving the current structure of the production system. Gantt charts and reports are applied for scheduling. Capacity planning problems related to coffee production are discussed. &nbsp;

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17

Qin, Ling, and Shu Lin Kan. "Production Dynamic Scheduling among Factories Based on Multi-Agent." Advanced Materials Research 466-467 (February 2012): 1386–91. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1386.

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To solve fluctuation problem in production plan and scheduling among factories, a logic framework of production dynamic scheduling among factories based on multi-agent technology was constructed. In this framework, the production dynamic scheduling multi-agent negotiation rules and mechanism among factories were established. Furthermore, the production dynamic scheduling multi-agent negotiation procedure among factories was investigated. Finally, the simulation system of production dynamic scheduling among factories based on multi-agent is demonstrated and validated by Flexsim software. It has shown the proposed method can improve the adaptability and stability of production plan and scheduling, and provide a support for optimal and dynamic production plan and scheduling among factories.

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18

Dani, YUNIAWAN, and ITO Teruaki. "411 Production Scheduling of Central Kitchen for Bakso Restaurant Chain." Proceedings of Conference of Chugoku-Shikoku Branch 2012.50 (2012): 41101–2. http://dx.doi.org/10.1299/jsmecs.2012.50.41101.

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19

Zhang, Guohua, Xiang Li, Yang Yang, and Minghuang Chen. "Study on Loading-Machine Production Scheduling Algorithm of Forest-Pulp-Paper Enterprise." Computer and Information Science 10, no. 2 (2017): 25. http://dx.doi.org/10.5539/cis.v10n2p25.

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Through the needs of Loading-Machine storage scheduling in forestry-pulp-paper production logistics intelligent distribution system, analysis the Loading-Machine scheduling model of problems and improvement measures, put forward Loading-Machine intelligent production scheduling algorithm, based on ensuring the feasibility of scheduling, scheduling to rationalization, equalization, execute only, production process optimization, and realize the Loading-Machine intelligent production scheduling through the computer programming.

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20

Водянова, Vyera Vodyanova, Ненашев, and Oleg Nenashev. "Basic Models of Production Process Scheduling." Administration 3, no. 2 (2015): 16–21. http://dx.doi.org/10.12737/11505.

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Paper has been devoted to methodological and theoretical ideas related to formation of hierarchy for basic dynamic models of production process scheduling which is an important component of quality control. However, the production process scheduling cannot be provided using universal modern simulation packages and requires the creation of special program, in which developed original algorithms for control of material and orders flows, and original scheduling algorithms are realized. For development of such original algorithms the hierarchy of material flows’ basic models may be useful. This hierarchy is based on hydraulic KT-model of elementary production link. In the paper the main directions of restrictions into the KT-model that allow expand basic models’ variety and create a flexible tool for production process scheduling model building have been presented.

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21

Zhang, Lie Ping, and Yun Sheng Zhang. "Research on Production Scheduling Problems in Process Industry Based on Ant Colony System." Advanced Materials Research 108-111 (May 2010): 519–24. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.519.

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In order to improve the production of process industry, the ant colony system(ACS) was applied to the production scheduling problem. Based on the analysis of the production scheduling problem for process industry, a production scheduling model was established, whose goal was to obtain the shortest total process time. The search strategy, heuristic information rules, pheromone updating mechanism, process step starting time and detailed algorithm implementation of ACS were discussed. Using a practical production scheduling problem as an example, the established model and designed algorithm were applied to implement the scheduling simulation. The simulation results show that the scheduling model and algorithm are feasible, and have a better scheduling performance than the stochastic scheduling method, and can be applied to solve practical production scheduling problem for process industry.

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22

Huang, Min, Ruixian Huang, Bo Sun, and Linrong Li. "Research on the Production Scheduling Optimization for Virtual Enterprises." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/492158.

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Production scheduling is a rather difficult problem in virtual enterprises (VE) for the tasks of production which would be executed by some distributed and independent members. Factors such as the timing constraints of task and ability restrictions of the members are considered comprehensibly to solve the global scheduling optimization problem. This paper establishes a partner selection model based on an improved ant colony algorithm at first, then presents a production scheduling framework with two layers as global scheduling and local scheduling for virtual enterprise, and gives a global scheduling mathematical model with the smallest total production time based on it. An improved genetic algorithm is proposed in the model to solve the time complexity of virtual enterprise production scheduling. The presented experimental results validate the optimization of the model and the efficiency of the algorithm.

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23

Duan, Jing, Jianjun Yu, and Guangwen Liang. "Food Production Enterprise Production Planning and Scheduling." Advance Journal of Food Science and Technology 10, no. 8 (2016): 558–62. http://dx.doi.org/10.19026/ajfst.10.2183.

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24

Trebuňa, Peter, and Miriam Pekarč’ková. "APP Method of Production Scheduling." Procedia Engineering 48 (2012): 679–83. http://dx.doi.org/10.1016/j.proeng.2012.09.570.

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25

Schulte, J. W., and B. D. Becker. "Production Scheduling Using Genetic Algorithms." IFAC Proceedings Volumes 25, no. 7 (1992): 367–72. http://dx.doi.org/10.1016/s1474-6670(17)52393-9.

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26

Rodríguez-Somoza, B., R. Galán, and E. A. Puente. "Production Scheduling Using AI Techniques." IFAC Proceedings Volumes 23, no. 3 (1990): 387–92. http://dx.doi.org/10.1016/s1474-6670(17)52588-4.

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27

Kumral, Mustafa. "Robust stochastic mine production scheduling." Engineering Optimization 42, no. 6 (2010): 567–79. http://dx.doi.org/10.1080/03052150903353336.

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28

Chryssolouris, G., N. Giannelos, N. Papakostas, and D. Mourtzis. "Chaos Theory in Production Scheduling." CIRP Annals 53, no. 1 (2004): 381–83. http://dx.doi.org/10.1016/s0007-8506(07)60721-5.

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29

Metaxiotis, Kostas S., John E. Psarras, and Kostas A. Ergazakis. "Production scheduling in ERP systems." Business Process Management Journal 9, no. 2 (2003): 221–47. http://dx.doi.org/10.1108/14637150310468416.

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30

Lane, Robin, and Stephen Evans. "Solving problems in production scheduling." Computer Integrated Manufacturing Systems 8, no. 2 (1995): 117–24. http://dx.doi.org/10.1016/0951-5240(95)00005-e.

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31

Kanet, John J., and Heimo H. Adelsberger. "Expert systems in production scheduling." European Journal of Operational Research 29, no. 1 (1987): 51–59. http://dx.doi.org/10.1016/0377-2217(87)90192-5.

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32

Buxey, Geoff. "Production scheduling: Practice and theory." European Journal of Operational Research 39, no. 1 (1989): 17–31. http://dx.doi.org/10.1016/0377-2217(89)90349-4.

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33

Matsumoto, Kazuki, Hiroyoshi Miwa, and Toshihide Ibaraki. "Scheduling of corrugated paper production." European Journal of Operational Research 192, no. 3 (2009): 782–92. http://dx.doi.org/10.1016/j.ejor.2007.10.019.

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34

Bhosale, Kailash Changdeorao, and Padmakar Jagannath Pawar. "Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities." Journal of Computational Design and Engineering 7, no. 6 (2020): 761–74. http://dx.doi.org/10.1093/jcde/qwaa055.

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Abstract Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of ₹2 23 324 for weeks 1–4 in overall cost compared with the results obtained by using CPLEX software.

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35

Lai, Ling Hong. "Mixed-Model Flow Production Scheduling Method Based on Multi-Agent and Hybrid Genetic Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 399–402. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.399.

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To solve the dynamic and complex problem of production scheduling, depending on the introduction between multi-agent and hybrid genetic algorithm in mixed-model flow production scheduling, this paper proposed a mixed-model flow production scheduling method based on multi-agent and hybrid genetic algorithm. On the basis of this model, the mixed-model flow production scheduling procedure and strategy based on multi-agent and hybrid genetic algorithm were established. Finally, mixed-model flow production scheduling simulation system based on multi-agent and hybrid genetic algorithm was demonstrated and validated by QUEST software. It has shown the proposed method can improve the benefit of production scheduling, and provide a support for adapting to complex and dynamic production scheduling in mixed-model flow production.

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36

Asmawar, Mellysa. "USULAN PENJADWALAN PRODUKSI PRODUK ST 37777 PT EBAKO NUSANTARA PADA DEPARTEMEN SMOOTHMILLING UNTUK MEMINIMASI MAKESPAN." J@ti Undip : Jurnal Teknik Industri 13, no. 1 (2018): 61. http://dx.doi.org/10.14710/jati.13.1.61-66.

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AbstrakProses produksi ST 37777 di PT Ebako Nusantara menggunakan jadwal yang didasarkan oleh proses-proses yang dilakukan dengan menggunakan data historis yang telah ada dari proses produksi yang telah dilakukan. PT Ebako Nusantara merupakan industri manufaktur yang bergerak di bidang furnitur yang berlokasi di Terboyo, Semarang, Jawa Tengah. Dalam proses produksi ST 37777, terdapat 11 mesin dan 16 job dimana setiap job memiliki urutan mesin yang berbeda. Penjadwalan yang ada untuk produk tipe ST 37777 dengan tipe jobshop belum menerapkan suatu ketetapan dalam penentuan waktu dan urutan pengerjaan mesin yang efektif sehingga masih banyak job yang selesai terlambat. Untuk itu diperlukan suatu penjadwalan mesin yang efektif sehingga dapat memenuhi waktu produksi pesanan sesuai dengan yang telah disepakati. Penjadwalan jobshop diperlukan untuk memaksimumkan efisiensi dan utilitas sumber daya di lantai produksi. Penentuan jadwal mesin ini bertujuan meminimasi makespan dengan menggunakan Software WINQSB modul job schedulling. Metode yang digunakan adalah metode Short Processing Time. Hasil penjadwalan menggunakan Software WINQSB diperoleh makespan menjadi 15 jam dengan hasil penjadwalan tersebut tidak ada job yang terlambat dan semua job dikerjakan berurutan. AbstractThe production process of ST 37777 in PT Ebako Nusantara uses a schedule based on the processes performed using existing historical data from the production process that has been done. PT Ebako Nusantara is a manufacturing industry engaged in furnitur located in Terboyo, Semarang, Central Java. In the production process ST 37777, there are 11 machines and 16 jobs where each job has a different sequence of machines. The existing scheduling for ST 37777 type product with jobshop type has not been applied a determination in the timing and sequence of effective machine work so that many jobs are finished too late. For that required an effective engine scheduling so that it can meet the production time of orders in accordance with the agreed. Jobshop scheduling is needed to maximize efficiency and resource utilities on the production floor. Determination of this machine schedule aims to minimize the makespan using WINQSB Software job scheduling module. The method used is the method of Short Processing Time. The scheduling result using WINQSB software obtained makespan to 15 hours with scheduling result no job is late and all job done in sequence. Keywords: Jobshop Scheduling; Short Processing Time; Makespan Minimization

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37

Pan, Feng Shan, Chun Ming Ye, and Ji Hua Zhou. "Re-Entrant Production Scheduling Problem under Uncertainty Based on QPSO Algorithm." Applied Mechanics and Materials 66-68 (July 2011): 1061–66. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1061.

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Production scheduling problem is the one of the most basic, important and difficult theoretical research in a manufacturing system. In the past decades of research, the classical scheduling theory has made signficant progress, but the actual scheduling problems are much more complicated than the classical theory. In order to study, the actual scheduling problems are often made much simpler. Therefore, it is difficult for the results of the classical scheduling theory to been put into practice. The considerable gap also exists between the classical scheduling theory and the actual scheduling problem. But with the increasingly fierce market competition, customers have become increasingly demanding product diversification, product life cycle is shorter and more sophisticated structure of the product makes the actual scheduling a large number of uncertainties, the traditional model has become difficult to obtain satisfactory results. This paper makes a analysis on the uncertainties of production scheduling,and tries to solve re-entrant production scheduling problem based on QPSO. It introduces the mathematical model and the solving process based on QPSO algorithm. Then it makes construction strategies to solve it. The paper simulates with Visual C. The results show that this algorithm is feasibility.

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38

Zahedi, Zahedi, and Ashadi Salim. "Integrating Preventive Maintenance Scheduling As Probability Machine Failure And Batch Production Scheduling." ComTech: Computer, Mathematics and Engineering Applications 7, no. 2 (2016): 105. http://dx.doi.org/10.21512/comtech.v7i2.2247.

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This paper discusses integrated model of batch production scheduling and machine maintenance scheduling. Batch production scheduling uses minimize total actual flow time criteria and machine maintenance scheduling uses the probability of machine failure based on Weibull distribution. The model assumed no nonconforming parts in a planning horizon. The model shows an increase in the number of the batch (length of production run) up to a certain limit will minimize the total actual flow time. Meanwhile, an increase in the length of production run will implicate an increase in the number of PM. An example was given to show how the model and algorithm work.

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39

Bożek, Andrzej, and Marian Wysocki. "Off-Line and Dynamic Production Scheduling – A Comparative Case Study." Management and Production Engineering Review 7, no. 1 (2016): 21–32. http://dx.doi.org/10.1515/mper-2016-0003.

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Abstract A comprehensive case study of manufacturing scheduling solutions development is given. It includes highly generalized scheduling problem as well as a few scheduling modes, methods and problem models. The considered problem combines flexible job shop structure, lot streaming with variable sublots, transport times, setup times, and machine calendars. Tabu search metaheuristic and constraint programming methods have been used for the off-line scheduling. Two dynamic scheduling methods have also been implemented, i.e., dispatching rules for the completely reactive scheduling and a multi-agent system for the predictivereactive scheduling. In these implementations three distinct models of the problem have been used, based on: graph representation, optimal constraint satisfaction, and Petri net formalism. Each of these solutions has been verified in computational experiments. The results are compared and some findings about advantages, disadvantages, and suggestions on using the solutions are formulated.

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40

Chen, Xiaowu, Guozhang Jiang, Yongmao Xiao, Gongfa Li, and Feng Xiang. "A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED." Mathematics 9, no. 18 (2021): 2256. http://dx.doi.org/10.3390/math9182256.

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Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.

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41

Kujawski, K., and J. Świątek. "Electroplating production scheduling by cyclogram unfolding in dynamic hoist scheduling problem." International Journal of Production Research 49, no. 17 (2011): 5355–71. http://dx.doi.org/10.1080/00207543.2010.519733.

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42

Kurbel, Karl, and Andreas Ruppel. "Integrating intelligent job-scheduling into a real-world production-scheduling system." Journal of Intelligent Manufacturing 7, no. 5 (1996): 373–77. http://dx.doi.org/10.1007/bf00123913.

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43

Dodin, Bajis, and K. Huang Chan. "Application of production scheduling methods to external and internal audit scheduling." European Journal of Operational Research 52, no. 3 (1991): 267–79. http://dx.doi.org/10.1016/0377-2217(91)90162-o.

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44

Bierwirth, Christian, and Dirk C. Mattfeld. "Production Scheduling and Rescheduling with Genetic Algorithms." Evolutionary Computation 7, no. 1 (1999): 1–17. http://dx.doi.org/10.1162/evco.1999.7.1.1.

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A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.

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45

S, Saravanakumar. "Simultaneous Scheduling of Assembly and Production Shops Using GA based Heuristic." International Journal of Psychosocial Rehabilitation 24, no. 4 (2020): 6128–39. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020423.

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46

Gu, Jian, and Wei Min Mao. "A Production Scheduling Framework Integrated with Simulation Module." Advanced Materials Research 602-604 (December 2012): 1831–34. http://dx.doi.org/10.4028/www.scientific.net/amr.602-604.1831.

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It is necessary to achieve high system performances in terms of throughput rate and service level in today’s business environment. This can be achieved by implementing efficient and effective production planning methods complemented with precise and fast scheduling predictions. A framework is proposed to integrate real-time production data, scheduling mechanisms and simulation for providing realistic scheduling policies that could be used for operational and tactical decision-making. The focus is on the use of discrete event simulation utilizing relevant shop floor data, provided by an ERP system. A primary objective is to evaluate and characterize scheduling policies in a discrete manufacturing environment.

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KUSWANDI, IMRON. "MINIMASI MASKEPAN DENGAN PENJADWALAN PRODUKSI PADA TIPE PRODUKSI BERULANG." Jurnal Teknik Industri 11, no. 1 (2012): 84. http://dx.doi.org/10.22219/jtiumm.vol11.no1.84-93.

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Abstract:

Methodologically there are some problems in the methods of scheduling production whichhave been available. In the methods of scheduling production which have been available, it isoften less capable for giving the real condition images from the real systems. It is indicated bythe given assumption that each operation should be finished previously before the other operationsare done. This case is inappropriate if applied in the repetitive production types as happenedin X Gresik, Co. Ltd. Because methodologically there are some problems in the methodsof scheduling conventional production, so in this research the methods of scheduling conventionalproduction are modified by using Microsoft excel application software, so it enables inthis method to handle the case of scheduling production in the types of repetitive production.urthermore, by using the methods of scheduling production modified by using Microsoft excelapplication software, the scheduling can be achieved by the better makespan (makespan =471,17 hours), so the production facility utilities are also more optimal compared to productionscheduling results by conventional approach (makespan = 893,7 hours).

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Prasad, V. Rajendra, Mike Graul, Perakath Benjamin, Richard Mayer, and Patrick D. Cahill. "Resource-Constrained Shop-Level Scheduling in a Shipyard." Journal of Ship Production 19, no. 02 (2003): 65–75. http://dx.doi.org/10.5957/jsp.2003.19.2.65.

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Ship production planning and scheduling at higher levels do not explicitly consider scheduling details at the level of individual workshops. However, the schedule of major events in ship production is collectively influenced by the actual shop-level, short-interval production schedules, which depend on resource and material availability and also on the due dates and priorities of the workloads. This necessitates development of robust, resource-constrained, shop-level scheduling systems that can support higher-level schedules in ship production. WorkShip (Knowledge Based Systems, Inc., College Station, TX) is a software tool for scheduling workload over short, regular intervals in workshops of a shipyard. It is driven by a powerful scheduling engine that is based on a generic model of resource-constrained job-shop scheduling and an efficient scheduling technique. Similar scheduling systems are being developed in other shops so that all systems can be used in tandem to support higher-level scheduling and help achieve optimal productivity for the shipyard.

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Zhu, Bao Lin, and Shou Feng Ji. "Steelmaking-Hot Rolling Scheduling Model and Method for Integrated Management in Iron and Steel Enterprises." Advanced Materials Research 860-863 (December 2013): 3094–99. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.3094.

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Iron and steel production scheduling problems are different from general production scheduling in machine industry. They have to meet special demands of steel production process. The CCR production manner dramatically promotes the revolution in technology and management, especially to planning and scheduling. In this paper, a scheduling model is presented to integrate the three working procedures and the lagrangian relaxation technology is proposed to get the optimal solution of the scheduling model. Finally, numerical examples are given to demonstrate the effectiveness of the integrated model and method.

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Xiao, Can Jun, Jin Ming Li, and Jin Yao. "Semiconductor Assembly and Test Production Line Simulation Technology." Advanced Materials Research 490-495 (March 2012): 3562–67. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.3562.

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The technology to simulate the semiconductor production line, consider main factors that will impact the production, and get the output from the simulation system as the reference for production scheduling is presented in this paper. By initiating the simulation model, include the die delivery information, equipment occupation information and etc. base on the FIFO (first in first out) and other principles, simulated the discrete event with four categories and also for two basic actions. The prediction result aligned with the actual production by T test. Changing the scheduling schemes at the same initial state of product line, engineer could obtain the optimal scheduling scheme by the comparing different simulation results. This study is not only an efficient, visual scheduling method, but also it is the basis for product re-scheduling. And this technology has been deployed in one ATM (Assembly and Test Manufacturing factory) factory in Chengdu and gets positive feedback.

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